OpenRaiser/Intern-Atlas huggingface dataset:
BES with FEM: Building Energy Simulation using Finite Element Methods
Venue: arXiv cs.CE 2016
Year: 2016

An overall objective of energy efficiency in the built environment is to improve building and systems performances in terms of durability, comfort and economics. In order to predict, improve and meet a certain set of performance requirements related to the indoor climate of buildings and the associated energy demand, building energy simulation (BES) tools are indispensable. Due to the rapid development of FEM software and the Multiphysics approaches, it should possible to build and simulate full 3D models of buildings regarding the energy demand. The paper presents a methodology for performing building energy simulation with Comsol. The method was applied to an international test box experiment. The results showed an almost perfect agreement between the used BES model and Comsol. These preliminary results confirm the great opportunities to use FEM related software for building energy performance simulation.
Incorporating long-range consistency in CNN-based texture generation
Venue: arXiv cs.CV 2016
Year: 2016

Gatys et al. (2015) showed that pair-wise products of features in a convolutional network are a very effective representation of image textures. We propose a simple modification to that representation which makes it possible to incorporate long-range structure into image generation, and to render images that satisfy various symmetry constraints. We show how this can greatly improve rendering of regular textures and of images that contain other kinds of symmetric structure. We also present applications to inpainting and season transfer.
Design of Robust Adaptive Beamforming Algorithms Based on Low-Rank and Cross-Correlation Techniques
Venue: arXiv cs.CE 2016
Year: 2016

This work presents cost-effective low-rank techniques for designing robust adaptive beamforming (RAB) algorithms. The proposed algorithms are based on the exploitation of the cross-correlation between the array observation data and the output of the beamformer. Firstly, we construct a general linear equation considered in large dimensions whose solution yields the steering vector mismatch. Then, we employ the idea of the full orthogonalization method (FOM), an orthogonal Krylov subspace based method, to iteratively estimate the steering vector mismatch in a reduced-dimensional subspace, resulting in the proposed orthogonal Krylov subspace projection mismatch estimation (OKSPME) method. We also devise adaptive algorithms based on stochastic gradient (SG) and conjugate gradient (CG) techniques to update the beamforming weights with low complexity and avoid any costly matrix inversion. The main advantages of the proposed low-rank and mismatch estimation techniques are their cost-effectiveness when dealing with high dimension subspaces or large sensor arrays. Simulations results show excellent performance in terms of the output signal-to-interference-plus-noise ratio (SINR) of the beamformer among all the compared RAB methods.
Recoverable robust spanning tree problem under interval uncertainty representations
Venue: arXiv cs.DS 2016
Year: 2016

This paper deals with the recoverable robust spanning tree problem under interval uncertainty representations. A polynomial time, combinatorial algorithm for the recoverable spanning tree problem is first constructed. This problem generalizes the incremental spanning tree problem, previously discussed in literature. The algorithm built is then applied to solve the recoverable robust spanning tree problem, under the traditional interval uncertainty representation, in polynomial time. Moreover, the algorithm allows to obtain, under some mild assumptions about the uncertainty intervals,several approximation results for the recoverable robust spanning tree problem under the Bertsimas and Sim interval uncertainty representation and the interval uncertainty representation with a budget constraint.
Virtualization Technology: Cross-VM Cache Side Channel Attacks make it Vulnerable
Venue: arXiv cs.CY 2016
Year: 2016

Cloud computing provides an effective business model for the deployment of IT infrastructure, platform, and software services. Often, facilities are outsourced to cloud providers and this offers the service consumer virtualization technologies without the added cost burden of development. However, virtualization introduces serious threats to service delivery such as Denial of Service (DoS) attacks, Cross-VM Cache Side Channel attacks, Hypervisor Escape and Hyper-jacking. One of the most sophisticated forms of attack is the cross-VM cache side channel attack that exploits shared cache memory between VMs. A cache side channel attack results in side channel data leakage, such as cryptographic keys. Various techniques used by the attackers to launch cache side channel attack are presented, as is a critical analysis of countermeasures against cache side channel attacks.
A Theoretical Approach to initiate Mobile Assisted Language Learning among school leavers and University Students of Sri Lanka
Venue: arXiv cs.CY 2016
Year: 2016

Mobile devices have proven to be an appropriate tool in the area of mobile language learning by expanding the horizons of learning beyond the classroom. The power of mobile devices and advanced technologies have created ease of access to educational resources and material to learners. This study is a part of an ongoing research focusing on enhancing English language among school leavers and undergraduates. The objective of this paper is to present the results of a study conducted to identify the prospects of m-learning among the target study community, who were school leavers and undergraduates of Sri Lanka. A design based research methodology will be used in this study along with activity theory as a framework in order to analyse the situation and find out the prospects in introducing m-learning among the target community.
Andro-profiler: Detecting and Classifying Android Malware based on Behavioral Profiles
Venue: arXiv cs.CR 2016
Year: 2016

Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, off-device, and hybrid approaches. In this paper, we contribute to the mobile security defense posture by introducing Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls, which are implicitly equivalent to distinct behavior characteristics. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than $98\%$, outperforms the existing state-of-the-art work, and is capable of identifying zero-day mobile malware samples.
Exploring Risk and Fraud Scenarios in Affiliate Marketing Technologies from the Advertisers perspective
Venue: arXiv cs.CY 2016
Year: 2016

Affiliate Marketing (AM) has become an important and cost effective tool for e-commerce. There are numerous risks and vulnerabilities that are typically associated with AM. Though a well-planned AM model can greatly benefit the e-commerce strategies of an enterprise, a haphazardly implemented system can expose a business enterprise to major risks and vulnerabilities, which can lead to great financial losses through fraudulent activities. This research-in-progress has identified some of the risks and the technical background of those scenarios. The research will now move on to build a functional prototype of an AM network to design and test solutions to control the identified risks.
Visualizing the invisible the relentless pursuit of MTech Imaging
Venue: arXiv cs.CY 2016
Year: 2016

This teaching case describes the challenges faced by MTech Imaging, a Singapore small and medium enterprise (SME) that specializes in providing thermal imaging solutions. In recent years, the company has relentlessly strived to become a digital innovative solution provider. This push has led to the development of a disruptive digital innovation called the AXION platform. Students are provided with vivid accounts of the journey undertaken by MTech to develop the AXION platform, the industry it competes in, and the challenges it faces in attempting to disrupt its industry through the introduction of the AXION platform. The case seeks to achieve three learning objectives: (1) allow students to learn from MTechs experiences in developing disruptive digital innovation; (2) immerse students as senior management of MTech to substantiate the best digital innovation strategy to adopt in order to disrupt its industry; and (3) expose students to the challenges of driving the adoption of such digital innovation in the market. It is hoped that the case can inspire students to become effective digital innovation entrepreneurs.
How to be a Good Frand in China: an Exploratory Study of Effective Social Media Behaviours for Foreign Brands
Venue: arXiv cs.CY 2016
Year: 2016

Many foreign companies see social media as a low cost marketing space for entering the vast Chinese market. This is fraught with complexity, requiring an understanding of social media behaviour, social media marketing, and the Chinese context. In this exploratory pilot study, we develop the notion of franding (online freinding of brands) as a unifying metaphor, and suggest that effective social media brand strategies resemble effective online friend behaviours. Based on this we develop some propositions and evaluate them qualitatively in interviews with young Chinese consumers. Our study suggests that conceptualizing brand social media relationships as a type of online friendship can assist in harmonizing disparate literature and provide useful insights for organizations operating in this complex area.
Appropriation of social media for knowledge sharing by patients with chronic illness
Venue: arXiv cs.CY 2016
Year: 2016

Social media technologies are increasingly utilized by patients, leading to development of online social groups where patients share experiences and offer support to their peers on these platforms. There is limited research investigating actual use of social media platforms by patients, issues faced in using such platforms and how appropriation of these platforms impact patient outcomes. A conceptual model based on social support theory and model of technology appropriation is proposed in order to investigate factors that influence this phenomenon. The authors propose that social support theory and model of technology appropriation could explain the determining factors, both enablers and barriers that drive appropriation and knowledge sharing behaviours of patients on social media platforms.
Liveness of Randomised Parameterised Systems under Arbitrary Schedulers (Technical Report)
Venue: arXiv cs.LO 2016
Year: 2016

We consider the problem of verifying liveness for systems with a finite, but unbounded, number of processes, commonly known as parameterised systems. Typical examples of such systems include distributed protocols (e.g. for the dining philosopher problem). Unlike the case of verifying safety, proving liveness is still considered extremely challenging, especially in the presence of randomness in the system. In this paper we consider liveness under arbitrary (including unfair) schedulers, which is often considered a desirable property in the literature of self-stabilising systems. We introduce an automatic method of proving liveness for randomised parameterised systems under arbitrary schedulers. Viewing liveness as a two-player reachability game (between Scheduler and Process), our method is a CEGAR approach that synthesises a progress relation for Process that can be symbolically represented as a finite-state automaton. The method is incremental and exploits both Angluin-style L*-learning and SAT-solvers. Our experiments show that our algorithm is able to prove liveness automatically for well-known randomised distributed protocols, including Lehmann-Rabin Randomised Dining Philosopher Protocol and randomised self-stabilising protocols (such as the Israeli-Jalfon Protocol). To the best of our knowledge, this is the first fully-automatic method that can prove liveness for randomised protocols.
IT outsourcing decision factors in research and practice: a case study
Venue: arXiv cs.CY 2016
Year: 2016

The concurrent effect of various internal and external factors on IT Outsourcing (ITO) decision making has seldom been investigated in a single study. Furthermore, research on external factors is scarce and there is no comprehensive theory that can fully explain ITO decisions made in practice. This paper explains how key decision factors, both internal and external, influence ITO decision making in a large Australian University. We also tested the feasibility of a highly regarded descriptive model of ITO decisions as the basic foundation of an ITO decision theory. The model failed to fully explain ITO decisions in our case organisation. We draw researchers' attention to the need for more exploration of external factors as well as clarification of contingency factors that may explain inconsistencies between ITO decision theories and practice, and call for more research for 'practicable' ITO decision aids. Implications for practice are also discussed in the paper.
Pairwise Quantization
Venue: arXiv cs.CV 2016
Year: 2016

We consider the task of lossy compression of high-dimensional vectors through quantization. We propose the approach that learns quantization parameters by minimizing the distortion of scalar products and squared distances between pairs of points. This is in contrast to previous works that obtain these parameters through the minimization of the reconstruction error of individual points. The proposed approach proceeds by finding a linear transformation of the data that effectively reduces the minimization of the pairwise distortions to the minimization of individual reconstruction errors. After such transformation, any of the previously-proposed quantization approaches can be used. Despite the simplicity of this transformation, the experiments demonstrate that it achieves considerable reduction of the pairwise distortions compared to applying quantization directly to the untransformed data.
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation
Venue: arXiv cs.NE 2016
Year: 2016

The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations. While simulations of these models recapitulate the ventral stream's progression from early view-specific to late view-tolerant representations, they fail to generate the most salient property of the intermediate representation for faces found in the brain: mirror-symmetric tuning of the neural population to head orientation. Here we prove that a class of hierarchical architectures and a broad set of biologically plausible learning rules can provide approximate invariance at the top level of the network. While most of the learning rules do not yield mirror-symmetry in the mid-level representations, we characterize a specific biologically-plausible Hebb-type learning rule that is guaranteed to generate mirror-symmetric tuning to faces tuning at intermediate levels of the architecture.
Effect of Densification on Cellular Network Performance with Bounded Pathloss Model
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we investigate how network densification influences the performance of downlink cellular network in terms of coverage probability (CP) and area spectral efficiency (ASE). Instead of the simplified unbounded pathloss model (UPM), we apply a more realistic bounded pathloss model (BPM) to model the decay of signal power caused by pathloss. It is shown that network densification indeed degrades CP when the base station (BS) density $λ$ is sufficiently large. This is inconsistent with the result derived using UPM that CP is independent of $λ$. Moreover, we shed light on the impact of ultra-dense deployment of BSs on the ASE scaling law. Specifically, it is proved that the cellular network ASE scales with rate $λe^{-κλ}$, i.e., first increases with $λ$ and then diminishes to be zero as $λ$ goes to infinity.
shapeDTW: shape Dynamic Time Warping
Venue: arXiv cs.CV 2016
Year: 2016

Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does not necessarily achieve locally sensible matchings. Concretely, two temporal points with entirely dissimilar local structures may be matched by DTW. To address this problem, we propose an improved alignment algorithm, named shape Dynamic Time Warping (shapeDTW), which enhances DTW by taking point-wise local structural information into consideration. shapeDTW is inherently a DTW algorithm, but additionally attempts to pair locally similar structures and to avoid matching points with distinct neighborhood structures. We apply shapeDTW to align audio signal pairs having ground-truth alignments, as well as artificially simulated pairs of aligned sequences, and obtain quantitatively much lower alignment errors than DTW and its two variants. When shapeDTW is used as a distance measure in a nearest neighbor classifier (NN-shapeDTW) to classify time series, it beats DTW on 64 out of 84 UCR time series datasets, with significantly improved classification accuracies. By using a properly designed local structure descriptor, shapeDTW improves accuracies by more than 10% on 18 datasets. To the best of our knowledge, shapeDTW is the first distance measure under the nearest neighbor classifier scheme to significantly outperform DTW, which had been widely recognized as the best distance measure to date. Our code is publicly accessible at: https://github.com/jiapingz/shapeDTW.
Position-Indexed Formulations for Kidney Exchange
Venue: arXiv cs.DS 2016
Year: 2016

A kidney exchange is an organized barter market where patients in need of a kidney swap willing but incompatible donors. Determining an optimal set of exchanges is theoretically and empirically hard. Traditionally, exchanges took place in cycles, with each participating patient-donor pair both giving and receiving a kidney. The recent introduction of chains, where a donor without a paired patient triggers a sequence of donations without requiring a kidney in return, increased the efficacy of fielded kidney exchanges---while also dramatically raising the empirical computational hardness of clearing the market in practice. While chains can be quite long, unbounded-length chains are not desirable: planned donations can fail before transplant for a variety of reasons, and the failure of a single donation causes the rest of that chain to fail, so parallel shorter chains are better in practice. In this paper, we address the tractable clearing of kidney exchanges with short cycles and chains that are long but bounded. This corresponds to the practice at most modern fielded kidney exchanges. We introduce three new integer programming formulations, two of which are compact. Furthermore, one of these models has a linear programming relaxation that is exactly as tight as the previous tightest formulation (which was not compact) for instances in which each donor has a paired patient. On real data from the UNOS nationwide exchange in the United States and the NLDKSS nationwide exchange in the United Kingdom, as well as on generated realistic large-scale data, we show that our new models are competitive with all existing solvers---in many cases outperforming all other solvers by orders of magnitude.
Cutsize Distributions of Balanced Hypergraph Bipartitions for Random Hypergraphs
Venue: arXiv cs.IT 2016
Year: 2016

In a previous work, we presented a parallel encoding algorithm for low-density parity-check (LDPC) codes by partitioning hypergraph representation for the LDPC codes. The aim of this research is to analyze the processing time of this encoding algorithm. This paper clarifies that the processing time of the encoding algorithm depends on the minimum cutsize of balanced hypergraph partitions. Moreover, this paper gives the typical minimum cutsize and cutsize distribution for balanced hypergraph bipartitions of random hypergraphs defined from a regular LDPC ensemble.
Trustworthy and Secure Service-Oriented Architecture for the Internet of Things
Venue: arXiv cs.CR 2016
Year: 2016

In the Internet of Things (IoT), heterogeneous devices connect to each other and to external systems to exchange data and provide services. Given the diversity of devices, it is becoming increasingly common to establish collaborative relationships between devices to provide composite services. However, due to the high degree of heterogeneity in the IoT context, one of the most significant challenges is to develop software applications that can run on a wide variety of devices and can communicate and collaborate with an even wider array of systems. A common middleware infrastructure for these devices will therefore have a significant impact on the design, deployment, and use of services in IoT systems by allowing developers to focus on the applications rather than the low-level implementation details each device.
Information Rates of Next-Generation Long-Haul Optical Fiber Systems Using Coded Modulation
Venue: arXiv cs.IT 2016
Year: 2016

A comprehensive study of the coded performance of long-haul spectrally-efficient WDM optical fiber transmission systems with different coded modulation decoding structures is presented. Achievable information rates are derived for three different square QAM formats and the optimal format is identified as a function of distance and specific decoder implementation. The four cases analyzed combine hard-decision (HD) or soft-decision (SD) decoding together with either a bit-wise or a symbol-wise demapper, the last two suitable for binary and nonbinary codes, respectively. The information rates achievable for each scheme are calculated based on the mismatched decoder principle. These quantities represent true indicators of the coded performance of the system for specific decoder implementations and when the modulation format and its input distribution are fixed. In combination with the structure of the decoder, two different receiver-side equalization strategies are also analyzed: electronic dispersion compensation and digital backpropagation. We show that, somewhat unexpectedly, schemes based on nonbinary HD codes can achieve information rates comparable to SD decoders and that, when SD is used, switching from a symbol-wise to a bit-wise decoder results in a negligible penalty. Conversely, from an information-theoretic standpoint, HD binary decoders are shown to be unsuitable for spectrally-efficient, long-haul systems.